Multiple Comparisons Using R (Hardcover)

Adopting a unifying theme based on maximum statistics, Multiple Comparisons Using R describes the common underlying theory of multiple comparison procedures through numerous examples. It also presents a detailed description of available software implementations in R. The R packages and source code for the analyses are available at http://CRAN.R-project.org

After giving examples of multiplicity problems, the book covers general concepts and basic multiple comparisons procedures, including the Bonferroni method and Simes’ test. It then shows how to perform parametric multiple comparisons in standard linear models and general parametric models. It also introduces the multcomp package in R, which offers a convenient interface to perform multiple comparisons in a general context. Following this theoretical framework, the book explores applications involving the Dunnett test, Tukey’s all pairwise comparisons, and general multiple contrast tests for standard regression models, mixed-effects models, and parametric survival models. The last chapter reviews other multiple comparison procedures, such as resampling-based procedures, methods for group sequential or adaptive designs, and the combination of multiple comparison procedures with modeling techniques.

Controlling multiplicity in experiments ensures better decision making and safeguards against false claims. A self-contained introduction to multiple comparison procedures, this book offers strategies for constructing the procedures and illustrates the framework for multiple hypotheses testing in general parametric models. It is suitable for readers with R experience but limited knowledge of multiple comparison procedures and vice versa.

《使用 R 進行多重比較》採用了基於最大統計的統一主題，通過眾多實例描述了多重比較程序的共同基本理論。同時，它還詳細介紹了 R 中可用的軟件實現。分析所需的 R 套件和源代碼可在 http://CRAN.R-project.org 上獲得。

在提供多重性問題示例後，本書涵蓋了一般概念和基本的多重比較程序，包括 Bonferroni 方法和 Simes 測試。然後，它展示了如何在標準線性模型和一般參數模型中進行參數多重比較。它還介紹了 R 中的 multcomp 套件，該套件提供了在一般情況下進行多重比較的便捷接口。在這個理論框架下，本書探討了涉及 Dunnett 測試、Tukey 的兩兩比較以及標準回歸模型、混合效應模型和參數生存模型的一般多重對比測試的應用。最後一章回顧了其他多重比較程序，如基於重抽樣的程序、用於群組序列或自適應設計的方法，以及將多重比較程序與建模技術結合的方法。

控制實驗中的多重性可以確保更好的決策和防止虛假聲稱。作為一本獨立的多重比較程序介紹，本書提供了構建程序的策略，並展示了在一般參數模型中進行多重假設檢驗的框架。適合具有 R 經驗但對多重比較程序知識有限的讀者，反之亦然。